Overview

Dataset statistics

Number of variables22
Number of observations980
Missing cells410
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory168.6 KiB
Average record size in memory176.1 B

Variable types

Categorical8
Text1
Numeric13

Alerts

price is highly overall correlated with avg_rating and 8 other fieldsHigh correlation
avg_rating is highly overall correlated with price and 11 other fieldsHigh correlation
processor_speed is highly overall correlated with price and 9 other fieldsHigh correlation
battery_capacity is highly overall correlated with brand_nameHigh correlation
fast_charging is highly overall correlated with price and 8 other fieldsHigh correlation
ram_capacity is highly overall correlated with price and 11 other fieldsHigh correlation
internal_memory is highly overall correlated with price and 9 other fieldsHigh correlation
screen_size is highly overall correlated with brand_nameHigh correlation
refresh_rate is highly overall correlated with price and 9 other fieldsHigh correlation
primary_camera_rear is highly overall correlated with avg_rating and 4 other fieldsHigh correlation
primary_camera_front is highly overall correlated with price and 5 other fieldsHigh correlation
resolution_height is highly overall correlated with price and 9 other fieldsHigh correlation
resolution_width is highly overall correlated with price and 6 other fieldsHigh correlation
brand_name is highly overall correlated with battery_capacity and 5 other fieldsHigh correlation
5G_or_not is highly overall correlated with avg_rating and 8 other fieldsHigh correlation
processor_brand is highly overall correlated with brand_name and 3 other fieldsHigh correlation
num_cores is highly overall correlated with processor_speed and 4 other fieldsHigh correlation
fast_charging_available is highly overall correlated with avg_rating and 6 other fieldsHigh correlation
num_rear_cameras is highly overall correlated with fast_charging_availableHigh correlation
os is highly overall correlated with brand_name and 2 other fieldsHigh correlation
extended_memory_available is highly overall correlated with processor_speed and 2 other fieldsHigh correlation
num_cores is highly imbalanced (70.4%)Imbalance
os is highly imbalanced (77.0%)Imbalance
avg_rating has 101 (10.3%) missing valuesMissing
processor_brand has 20 (2.0%) missing valuesMissing
processor_speed has 42 (4.3%) missing valuesMissing
battery_capacity has 11 (1.1%) missing valuesMissing
fast_charging has 211 (21.5%) missing valuesMissing
os has 14 (1.4%) missing valuesMissing
model has unique valuesUnique

Reproduction

Analysis started2023-10-22 10:44:19.545082
Analysis finished2023-10-22 10:44:50.833629
Duration31.29 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

brand_name
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
xiaomi
134 
samsung
132 
vivo
111 
realme
97 
oppo
88 
Other values (41)
418 

Length

Max length9
Median length8
Mean length5.5357143
Min length2

Characters and Unicode

Total characters5425
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.1%

Sample

1st rowapple
2nd rowapple
3rd rowapple
4th rowapple
5th rowapple

Common Values

ValueCountFrequency (%)
xiaomi 134
13.7%
samsung 132
13.5%
vivo 111
11.3%
realme 97
9.9%
oppo 88
9.0%
motorola 52
 
5.3%
apple 46
 
4.7%
oneplus 42
 
4.3%
poco 41
 
4.2%
tecno 33
 
3.4%
Other values (36) 204
20.8%

Length

2023-10-22T16:14:51.020676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xiaomi 134
13.7%
samsung 132
13.5%
vivo 111
11.3%
realme 97
9.9%
oppo 88
9.0%
motorola 52
 
5.3%
apple 46
 
4.7%
oneplus 42
 
4.3%
poco 41
 
4.2%
tecno 33
 
3.4%
Other values (36) 204
20.8%

Most occurring characters

ValueCountFrequency (%)
o 907
16.7%
i 570
10.5%
a 522
9.6%
m 424
 
7.8%
e 386
 
7.1%
p 352
 
6.5%
s 331
 
6.1%
n 322
 
5.9%
l 293
 
5.4%
v 233
 
4.3%
Other values (16) 1085
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5425
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 907
16.7%
i 570
10.5%
a 522
9.6%
m 424
 
7.8%
e 386
 
7.1%
p 352
 
6.5%
s 331
 
6.1%
n 322
 
5.9%
l 293
 
5.4%
v 233
 
4.3%
Other values (16) 1085
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5425
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 907
16.7%
i 570
10.5%
a 522
9.6%
m 424
 
7.8%
e 386
 
7.1%
p 352
 
6.5%
s 331
 
6.1%
n 322
 
5.9%
l 293
 
5.4%
v 233
 
4.3%
Other values (16) 1085
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 907
16.7%
i 570
10.5%
a 522
9.6%
m 424
 
7.8%
e 386
 
7.1%
p 352
 
6.5%
s 331
 
6.1%
n 322
 
5.9%
l 293
 
5.4%
v 233
 
4.3%
Other values (16) 1085
20.0%

model
Text

UNIQUE 

Distinct980
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:51.390225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length50
Median length39
Mean length20.370408
Min length6

Characters and Unicode

Total characters19963
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique980 ?
Unique (%)100.0%

Sample

1st rowApple iPhone 11
2nd rowApple iPhone 11 (128GB)
3rd rowApple iPhone 11 Pro Max
4th rowApple iPhone 12
5th rowApple iPhone 12 (128GB)
ValueCountFrequency (%)
5g 307
 
7.1%
ram 221
 
5.1%
215
 
5.0%
pro 202
 
4.7%
128gb 135
 
3.1%
xiaomi 134
 
3.1%
samsung 132
 
3.0%
galaxy 132
 
3.0%
vivo 111
 
2.6%
redmi 103
 
2.4%
Other values (514) 2639
60.9%
2023-10-22T16:14:52.047004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3351
 
16.8%
o 1240
 
6.2%
G 1007
 
5.0%
i 885
 
4.4%
a 876
 
4.4%
e 813
 
4.1%
1 608
 
3.0%
P 585
 
2.9%
2 539
 
2.7%
l 515
 
2.6%
Other values (56) 9544
47.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7890
39.5%
Uppercase Letter 5088
25.5%
Space Separator 3351
16.8%
Decimal Number 2912
 
14.6%
Close Punctuation 250
 
1.3%
Open Punctuation 250
 
1.3%
Math Symbol 221
 
1.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 1007
19.8%
P 585
11.5%
B 473
9.3%
R 469
9.2%
M 408
8.0%
A 402
 
7.9%
O 293
 
5.8%
S 236
 
4.6%
X 207
 
4.1%
N 183
 
3.6%
Other values (16) 825
16.2%
Lowercase Letter
ValueCountFrequency (%)
o 1240
15.7%
i 885
11.2%
a 876
11.1%
e 813
10.3%
l 515
 
6.5%
m 502
 
6.4%
n 463
 
5.9%
r 407
 
5.2%
s 290
 
3.7%
t 285
 
3.6%
Other values (15) 1614
20.5%
Decimal Number
ValueCountFrequency (%)
1 608
20.9%
2 539
18.5%
5 502
17.2%
8 257
8.8%
0 236
 
8.1%
6 223
 
7.7%
3 199
 
6.8%
4 192
 
6.6%
9 93
 
3.2%
7 63
 
2.2%
Space Separator
ValueCountFrequency (%)
3351
100.0%
Close Punctuation
ValueCountFrequency (%)
) 250
100.0%
Open Punctuation
ValueCountFrequency (%)
( 250
100.0%
Math Symbol
ValueCountFrequency (%)
+ 221
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12978
65.0%
Common 6985
35.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1240
 
9.6%
G 1007
 
7.8%
i 885
 
6.8%
a 876
 
6.7%
e 813
 
6.3%
P 585
 
4.5%
l 515
 
4.0%
m 502
 
3.9%
B 473
 
3.6%
R 469
 
3.6%
Other values (41) 5613
43.3%
Common
ValueCountFrequency (%)
3351
48.0%
1 608
 
8.7%
2 539
 
7.7%
5 502
 
7.2%
8 257
 
3.7%
) 250
 
3.6%
( 250
 
3.6%
0 236
 
3.4%
6 223
 
3.2%
+ 221
 
3.2%
Other values (5) 548
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3351
 
16.8%
o 1240
 
6.2%
G 1007
 
5.0%
i 885
 
4.4%
a 876
 
4.4%
e 813
 
4.1%
1 608
 
3.0%
P 585
 
2.9%
2 539
 
2.7%
l 515
 
2.6%
Other values (56) 9544
47.8%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct379
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32520.504
Minimum3499
Maximum650000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:52.246526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3499
5-th percentile7395.05
Q112999
median19994.5
Q335491.5
95-th percentile99990.45
Maximum650000
Range646501
Interquartile range (IQR)22492.5

Descriptive statistics

Standard deviation39531.813
Coefficient of variation (CV)1.2155966
Kurtosis79.191922
Mean32520.504
Median Absolute Deviation (MAD)8995.5
Skewness6.591791
Sum31870094
Variance1.5627642 × 109
MonotonicityNot monotonic
2023-10-22T16:14:52.439480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14999 21
 
2.1%
11999 17
 
1.7%
19990 17
 
1.7%
19999 16
 
1.6%
16999 16
 
1.6%
17999 15
 
1.5%
15999 15
 
1.5%
13999 15
 
1.5%
8999 14
 
1.4%
12999 14
 
1.4%
Other values (369) 820
83.7%
ValueCountFrequency (%)
3499 1
0.1%
3890 1
0.1%
3990 1
0.1%
3999 1
0.1%
4499 1
0.1%
4649 1
0.1%
4787 1
0.1%
4999 1
0.1%
5249 1
0.1%
5299 1
0.1%
ValueCountFrequency (%)
650000 1
0.1%
480000 1
0.1%
239999 1
0.1%
214990 1
0.1%
199990 1
0.1%
182999 1
0.1%
179900 1
0.1%
172999 1
0.1%
169900 1
0.1%
169000 1
0.1%

avg_rating
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)3.4%
Missing101
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean7.8258248
Minimum6
Maximum8.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:52.625689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.3
Q17.4
median8
Q38.4
95-th percentile8.8
Maximum8.9
Range2.9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.74028543
Coefficient of variation (CV)0.094595197
Kurtosis-0.30068137
Mean7.8258248
Median Absolute Deviation (MAD)0.5
Skewness-0.6989993
Sum6878.9
Variance0.54802253
MonotonicityNot monotonic
2023-10-22T16:14:52.805774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8.4 60
 
6.1%
8.2 58
 
5.9%
8.3 55
 
5.6%
7.5 53
 
5.4%
8.5 50
 
5.1%
8 49
 
5.0%
8.6 47
 
4.8%
7.9 45
 
4.6%
7.7 38
 
3.9%
7.8 36
 
3.7%
Other values (20) 388
39.6%
(Missing) 101
 
10.3%
ValueCountFrequency (%)
6 12
1.2%
6.1 15
1.5%
6.2 11
1.1%
6.3 10
1.0%
6.4 10
1.0%
6.5 14
1.4%
6.6 15
1.5%
6.7 13
1.3%
6.8 13
1.3%
6.9 18
1.8%
ValueCountFrequency (%)
8.9 35
3.6%
8.8 26
2.7%
8.7 33
3.4%
8.6 47
4.8%
8.5 50
5.1%
8.4 60
6.1%
8.3 55
5.6%
8.2 58
5.9%
8.1 35
3.6%
8 49
5.0%

5G_or_not
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
1
549 
0
431 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 549
56.0%
0 431
44.0%

Length

2023-10-22T16:14:53.004884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:14:53.135204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 549
56.0%
0 431
44.0%

Most occurring characters

ValueCountFrequency (%)
1 549
56.0%
0 431
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 549
56.0%
0 431
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common 980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 549
56.0%
0 431
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 549
56.0%
0 431
44.0%

processor_brand
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)1.4%
Missing20
Missing (%)2.0%
Memory size7.8 KiB
snapdragon
413 
helio
201 
dimensity
177 
exynos
50 
bionic
45 
Other values (8)
74 

Length

Max length10
Median length9
Mean length8.053125
Min length5

Characters and Unicode

Total characters7731
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowbionic
2nd rowbionic
3rd rowbionic
4th rowbionic
5th rowbionic

Common Values

ValueCountFrequency (%)
snapdragon 413
42.1%
helio 201
20.5%
dimensity 177
18.1%
exynos 50
 
5.1%
bionic 45
 
4.6%
unisoc 26
 
2.7%
tiger 24
 
2.4%
google 9
 
0.9%
kirin 7
 
0.7%
spreadtrum 4
 
0.4%
Other values (3) 4
 
0.4%
(Missing) 20
 
2.0%

Length

2023-10-22T16:14:53.277868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
snapdragon 413
43.0%
helio 201
20.9%
dimensity 177
18.4%
exynos 50
 
5.2%
bionic 45
 
4.7%
unisoc 26
 
2.7%
tiger 24
 
2.5%
google 9
 
0.9%
kirin 7
 
0.7%
spreadtrum 4
 
0.4%
Other values (3) 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 1132
14.6%
a 833
10.8%
o 754
9.8%
i 711
9.2%
s 673
8.7%
d 595
7.7%
e 467
 
6.0%
g 455
 
5.9%
r 452
 
5.8%
p 417
 
5.4%
Other values (15) 1242
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7723
99.9%
Decimal Number 8
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1132
14.7%
a 833
10.8%
o 754
9.8%
i 711
9.2%
s 673
8.7%
d 595
7.7%
e 467
 
6.0%
g 455
 
5.9%
r 452
 
5.9%
p 417
 
5.4%
Other values (11) 1234
16.0%
Decimal Number
ValueCountFrequency (%)
9 2
25.0%
8 2
25.0%
6 2
25.0%
3 2
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7723
99.9%
Common 8
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1132
14.7%
a 833
10.8%
o 754
9.8%
i 711
9.2%
s 673
8.7%
d 595
7.7%
e 467
 
6.0%
g 455
 
5.9%
r 452
 
5.9%
p 417
 
5.4%
Other values (11) 1234
16.0%
Common
ValueCountFrequency (%)
9 2
25.0%
8 2
25.0%
6 2
25.0%
3 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1132
14.6%
a 833
10.8%
o 754
9.8%
i 711
9.2%
s 673
8.7%
d 595
7.7%
e 467
 
6.0%
g 455
 
5.9%
r 452
 
5.8%
p 417
 
5.4%
Other values (15) 1242
16.1%

num_cores
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing6
Missing (%)0.6%
Memory size7.8 KiB
8.0
899 
6.0
 
39
4.0
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2922
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.0
2nd row6.0
3rd row6.0
4th row6.0
5th row6.0

Common Values

ValueCountFrequency (%)
8.0 899
91.7%
6.0 39
 
4.0%
4.0 36
 
3.7%
(Missing) 6
 
0.6%

Length

2023-10-22T16:14:53.450560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:14:53.641871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
8.0 899
92.3%
6.0 39
 
4.0%
4.0 36
 
3.7%

Most occurring characters

ValueCountFrequency (%)
. 974
33.3%
0 974
33.3%
8 899
30.8%
6 39
 
1.3%
4 36
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1948
66.7%
Other Punctuation 974
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 974
50.0%
8 899
46.1%
6 39
 
2.0%
4 36
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 974
33.3%
0 974
33.3%
8 899
30.8%
6 39
 
1.3%
4 36
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 974
33.3%
0 974
33.3%
8 899
30.8%
6 39
 
1.3%
4 36
 
1.2%

processor_speed
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)3.7%
Missing42
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean2.4272175
Minimum1.2
Maximum3.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:53.943547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.8
Q12.05
median2.3
Q32.84
95-th percentile3.2
Maximum3.22
Range2.02
Interquartile range (IQR)0.79

Descriptive statistics

Standard deviation0.46408956
Coefficient of variation (CV)0.1912023
Kurtosis-0.66666684
Mean2.4272175
Median Absolute Deviation (MAD)0.3
Skewness0.18833557
Sum2276.73
Variance0.21537912
MonotonicityNot monotonic
2023-10-22T16:14:54.252615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 146
14.9%
2.2 135
13.8%
2.4 128
13.1%
3.2 94
9.6%
2.3 86
8.8%
3 53
 
5.4%
2.84 36
 
3.7%
2.05 28
 
2.9%
2.5 23
 
2.3%
1.8 23
 
2.3%
Other values (25) 186
19.0%
(Missing) 42
 
4.3%
ValueCountFrequency (%)
1.2 1
 
0.1%
1.3 10
 
1.0%
1.4 5
 
0.5%
1.5 4
 
0.4%
1.6 20
 
2.0%
1.8 23
 
2.3%
1.82 10
 
1.0%
1.95 1
 
0.1%
1.99 1
 
0.1%
2 146
14.9%
ValueCountFrequency (%)
3.22 18
 
1.8%
3.2 94
9.6%
3.13 2
 
0.2%
3.1 15
 
1.5%
3.05 8
 
0.8%
3 53
5.4%
2.96 3
 
0.3%
2.9 13
 
1.3%
2.86 3
 
0.3%
2.85 19
 
1.9%

battery_capacity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)9.2%
Missing11
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean4817.7482
Minimum1821
Maximum22000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:54.736517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1821
5-th percentile3500
Q14500
median5000
Q35000
95-th percentile6000
Maximum22000
Range20179
Interquartile range (IQR)500

Descriptive statistics

Standard deviation1009.5401
Coefficient of variation (CV)0.20954604
Kurtosis154.67898
Mean4817.7482
Median Absolute Deviation (MAD)0
Skewness9.2609863
Sum4668398
Variance1019171.1
MonotonicityNot monotonic
2023-10-22T16:14:55.078693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 487
49.7%
4500 97
 
9.9%
6000 60
 
6.1%
4000 42
 
4.3%
4700 27
 
2.8%
4300 24
 
2.4%
4800 18
 
1.8%
4200 13
 
1.3%
5020 11
 
1.1%
4600 10
 
1.0%
Other values (79) 180
 
18.4%
(Missing) 11
 
1.1%
ValueCountFrequency (%)
1821 1
0.1%
1900 1
0.1%
2000 1
0.1%
2050 1
0.1%
2150 1
0.1%
2230 1
0.1%
2275 1
0.1%
2400 1
0.1%
2438 1
0.1%
2500 1
0.1%
ValueCountFrequency (%)
22000 1
 
0.1%
21000 1
 
0.1%
9800 1
 
0.1%
8000 1
 
0.1%
7000 6
 
0.6%
6000 60
6.1%
5600 1
 
0.1%
5500 4
 
0.4%
5200 3
 
0.3%
5180 1
 
0.1%

fast_charging_available
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
1
837 
0
143 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 837
85.4%
0 143
 
14.6%

Length

2023-10-22T16:14:55.314630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:14:55.456959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 837
85.4%
0 143
 
14.6%

Most occurring characters

ValueCountFrequency (%)
1 837
85.4%
0 143
 
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 837
85.4%
0 143
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
Common 980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 837
85.4%
0 143
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 837
85.4%
0 143
 
14.6%

fast_charging
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)4.2%
Missing211
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean46.126138
Minimum10
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:55.596255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q118
median33
Q366
95-th percentile120
Maximum240
Range230
Interquartile range (IQR)48

Descriptive statistics

Standard deviation34.27787
Coefficient of variation (CV)0.74313331
Kurtosis3.4102676
Mean46.126138
Median Absolute Deviation (MAD)15
Skewness1.6455706
Sum35471
Variance1174.9723
MonotonicityNot monotonic
2023-10-22T16:14:55.784860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
33 152
15.5%
18 128
13.1%
67 65
 
6.6%
25 53
 
5.4%
120 46
 
4.7%
15 43
 
4.4%
80 42
 
4.3%
66 37
 
3.8%
10 33
 
3.4%
30 32
 
3.3%
Other values (22) 138
14.1%
(Missing) 211
21.5%
ValueCountFrequency (%)
10 33
 
3.4%
15 43
 
4.4%
18 128
13.1%
19 1
 
0.1%
20 10
 
1.0%
21 2
 
0.2%
22 5
 
0.5%
25 53
5.4%
27 1
 
0.1%
30 32
 
3.3%
ValueCountFrequency (%)
240 1
 
0.1%
210 2
 
0.2%
200 1
 
0.1%
180 1
 
0.1%
165 1
 
0.1%
150 7
 
0.7%
135 1
 
0.1%
125 6
 
0.6%
120 46
4.7%
100 7
 
0.7%

ram_capacity
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5602041
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:55.947462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q38
95-th percentile12
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7443782
Coefficient of variation (CV)0.41833732
Kurtosis0.99536819
Mean6.5602041
Median Absolute Deviation (MAD)2
Skewness0.74589618
Sum6429
Variance7.5316118
MonotonicityNot monotonic
2023-10-22T16:14:56.103980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 339
34.6%
6 234
23.9%
4 217
22.1%
12 86
 
8.8%
3 54
 
5.5%
2 32
 
3.3%
16 9
 
0.9%
1 7
 
0.7%
18 2
 
0.2%
ValueCountFrequency (%)
1 7
 
0.7%
2 32
 
3.3%
3 54
 
5.5%
4 217
22.1%
6 234
23.9%
8 339
34.6%
12 86
 
8.8%
16 9
 
0.9%
18 2
 
0.2%
ValueCountFrequency (%)
18 2
 
0.2%
16 9
 
0.9%
12 86
 
8.8%
8 339
34.6%
6 234
23.9%
4 217
22.1%
3 54
 
5.5%
2 32
 
3.3%
1 7
 
0.7%

internal_memory
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.03673
Minimum8
Maximum1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:56.279995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile32
Q164
median128
Q3128
95-th percentile256
Maximum1024
Range1016
Interquartile range (IQR)64

Descriptive statistics

Standard deviation107.13452
Coefficient of variation (CV)0.75962136
Kurtosis24.315641
Mean141.03673
Median Absolute Deviation (MAD)0
Skewness3.8301285
Sum138216
Variance11477.805
MonotonicityNot monotonic
2023-10-22T16:14:56.444477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
128 523
53.4%
64 193
 
19.7%
256 157
 
16.0%
32 67
 
6.8%
512 22
 
2.2%
16 12
 
1.2%
1024 5
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
16 12
 
1.2%
32 67
 
6.8%
64 193
 
19.7%
128 523
53.4%
256 157
 
16.0%
512 22
 
2.2%
1024 5
 
0.5%
ValueCountFrequency (%)
1024 5
 
0.5%
512 22
 
2.2%
256 157
 
16.0%
128 523
53.4%
64 193
 
19.7%
32 67
 
6.8%
16 12
 
1.2%
8 1
 
0.1%

screen_size
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5367653
Minimum3.54
Maximum8.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:56.642684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.54
5-th percentile6.1
Q16.5
median6.58
Q36.67
95-th percentile6.8
Maximum8.03
Range4.49
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.34916162
Coefficient of variation (CV)0.053415046
Kurtosis17.475291
Mean6.5367653
Median Absolute Deviation (MAD)0.09
Skewness-2.116199
Sum6406.03
Variance0.12191384
MonotonicityNot monotonic
2023-10-22T16:14:56.845850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 119
 
12.1%
6.67 97
 
9.9%
6.7 93
 
9.5%
6.6 91
 
9.3%
6.43 43
 
4.4%
6.4 43
 
4.4%
6.58 40
 
4.1%
6.8 35
 
3.6%
6.78 33
 
3.4%
6.1 31
 
3.2%
Other values (69) 355
36.2%
ValueCountFrequency (%)
3.54 1
 
0.1%
4 1
 
0.1%
4.5 1
 
0.1%
4.7 5
0.5%
5 5
0.5%
5.2 1
 
0.1%
5.3 1
 
0.1%
5.4 4
0.4%
5.42 1
 
0.1%
5.45 2
 
0.2%
ValueCountFrequency (%)
8.03 4
0.4%
8.02 1
 
0.1%
8.01 1
 
0.1%
8 1
 
0.1%
7.92 1
 
0.1%
7.8 2
0.2%
7.6 3
0.3%
7.4 1
 
0.1%
7.2 1
 
0.1%
7.1 3
0.3%

refresh_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.256122
Minimum60
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:56.997141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile60
Q160
median90
Q3120
95-th percentile121.2
Maximum240
Range180
Interquartile range (IQR)60

Descriptive statistics

Standard deviation28.988052
Coefficient of variation (CV)0.31421277
Kurtosis-0.66553131
Mean92.256122
Median Absolute Deviation (MAD)30
Skewness0.29773297
Sum90411
Variance840.30716
MonotonicityNot monotonic
2023-10-22T16:14:57.137613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
60 368
37.6%
120 344
35.1%
90 219
22.3%
144 39
 
4.0%
165 9
 
0.9%
240 1
 
0.1%
ValueCountFrequency (%)
60 368
37.6%
90 219
22.3%
120 344
35.1%
144 39
 
4.0%
165 9
 
0.9%
240 1
 
0.1%
ValueCountFrequency (%)
240 1
 
0.1%
165 9
 
0.9%
144 39
 
4.0%
120 344
35.1%
90 219
22.3%
60 368
37.6%

num_rear_cameras
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
3
551 
2
208 
4
156 
1
65 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters980
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 551
56.2%
2 208
 
21.2%
4 156
 
15.9%
1 65
 
6.6%

Length

2023-10-22T16:14:57.326572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:14:57.478297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 551
56.2%
2 208
 
21.2%
4 156
 
15.9%
1 65
 
6.6%

Most occurring characters

ValueCountFrequency (%)
3 551
56.2%
2 208
 
21.2%
4 156
 
15.9%
1 65
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 551
56.2%
2 208
 
21.2%
4 156
 
15.9%
1 65
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 551
56.2%
2 208
 
21.2%
4 156
 
15.9%
1 65
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 551
56.2%
2 208
 
21.2%
4 156
 
15.9%
1 65
 
6.6%

os
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.3%
Missing14
Missing (%)1.4%
Memory size7.8 KiB
android
909 
ios
 
46
other
 
11

Length

Max length7
Median length7
Mean length6.7867495
Min length3

Characters and Unicode

Total characters6556
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowios
2nd rowios
3rd rowios
4th rowios
5th rowios

Common Values

ValueCountFrequency (%)
android 909
92.8%
ios 46
 
4.7%
other 11
 
1.1%
(Missing) 14
 
1.4%

Length

2023-10-22T16:14:57.653513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:14:57.788461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
android 909
94.1%
ios 46
 
4.8%
other 11
 
1.1%

Most occurring characters

ValueCountFrequency (%)
d 1818
27.7%
o 966
14.7%
i 955
14.6%
r 920
14.0%
a 909
13.9%
n 909
13.9%
s 46
 
0.7%
t 11
 
0.2%
h 11
 
0.2%
e 11
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6556
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1818
27.7%
o 966
14.7%
i 955
14.6%
r 920
14.0%
a 909
13.9%
n 909
13.9%
s 46
 
0.7%
t 11
 
0.2%
h 11
 
0.2%
e 11
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 6556
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1818
27.7%
o 966
14.7%
i 955
14.6%
r 920
14.0%
a 909
13.9%
n 909
13.9%
s 46
 
0.7%
t 11
 
0.2%
h 11
 
0.2%
e 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1818
27.7%
o 966
14.7%
i 955
14.6%
r 920
14.0%
a 909
13.9%
n 909
13.9%
s 46
 
0.7%
t 11
 
0.2%
h 11
 
0.2%
e 11
 
0.2%

primary_camera_rear
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.319286
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:57.921544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q124
median50
Q364
95-th percentile108
Maximum200
Range198
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.000968
Coefficient of variation (CV)0.65583141
Kurtosis6.0646694
Mean50.319286
Median Absolute Deviation (MAD)14
Skewness1.7705786
Sum49312.9
Variance1089.0639
MonotonicityNot monotonic
2023-10-22T16:14:58.114492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
50 333
34.0%
64 181
18.5%
13 116
 
11.8%
48 114
 
11.6%
108 80
 
8.2%
12 57
 
5.8%
8 39
 
4.0%
200 18
 
1.8%
16 17
 
1.7%
5 6
 
0.6%
Other values (8) 19
 
1.9%
ValueCountFrequency (%)
2 2
 
0.2%
5 6
 
0.6%
8 39
 
4.0%
12 57
5.8%
12.2 4
 
0.4%
13 116
11.8%
16 17
 
1.7%
20 3
 
0.3%
24 2
 
0.2%
40 1
 
0.1%
ValueCountFrequency (%)
200 18
 
1.8%
108 80
 
8.2%
64 181
18.5%
54 3
 
0.3%
50.3 3
 
0.3%
50 333
34.0%
48 114
 
11.6%
47.2 1
 
0.1%
40 1
 
0.1%
24 2
 
0.2%

primary_camera_front
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)1.9%
Missing5
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean16.589744
Minimum0
Maximum60
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:58.296515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median16
Q316
95-th percentile32
Maximum60
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.876944
Coefficient of variation (CV)0.65564266
Kurtosis2.2373327
Mean16.589744
Median Absolute Deviation (MAD)8
Skewness1.4392944
Sum16175
Variance118.3079
MonotonicityNot monotonic
2023-10-22T16:14:58.498147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
16 307
31.3%
8 178
18.2%
32 155
15.8%
5 119
 
12.1%
12 50
 
5.1%
13 42
 
4.3%
20 37
 
3.8%
10 25
 
2.6%
50 12
 
1.2%
60 10
 
1.0%
Other values (9) 40
 
4.1%
ValueCountFrequency (%)
0 1
 
0.1%
2 6
 
0.6%
5 119
 
12.1%
7 5
 
0.5%
8 178
18.2%
10 25
 
2.6%
11 6
 
0.6%
12 50
 
5.1%
13 42
 
4.3%
16 307
31.3%
ValueCountFrequency (%)
60 10
 
1.0%
50 12
 
1.2%
48 2
 
0.2%
44 8
 
0.8%
40 6
 
0.6%
32 155
15.8%
25 3
 
0.3%
24 3
 
0.3%
20 37
 
3.8%
16 307
31.3%

extended_memory_available
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
1
618 
0
362 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 618
63.1%
0 362
36.9%

Length

2023-10-22T16:14:58.715745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:14:58.854746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 618
63.1%
0 362
36.9%

Most occurring characters

ValueCountFrequency (%)
1 618
63.1%
0 362
36.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 618
63.1%
0 362
36.9%

Most occurring scripts

ValueCountFrequency (%)
Common 980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 618
63.1%
0 362
36.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 618
63.1%
0 362
36.9%

resolution_height
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2214.6633
Minimum480
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:59.018547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile1440
Q11612
median2400
Q32408
95-th percentile3120
Maximum3840
Range3360
Interquartile range (IQR)796

Descriptive statistics

Standard deviation516.48425
Coefficient of variation (CV)0.23321119
Kurtosis1.0007869
Mean2214.6633
Median Absolute Deviation (MAD)54
Skewness-0.69404011
Sum2170370
Variance266755.98
MonotonicityNot monotonic
2023-10-22T16:14:59.278575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 344
35.1%
1600 149
15.2%
2408 65
 
6.6%
2412 58
 
5.9%
2340 43
 
4.4%
2460 39
 
4.0%
3200 32
 
3.3%
720 20
 
2.0%
1612 18
 
1.8%
1080 16
 
1.6%
Other values (55) 196
20.0%
ValueCountFrequency (%)
480 3
 
0.3%
640 1
 
0.1%
720 20
2.0%
854 1
 
0.1%
960 1
 
0.1%
1080 16
1.6%
1280 2
 
0.2%
1334 4
 
0.4%
1440 5
 
0.5%
1480 1
 
0.1%
ValueCountFrequency (%)
3840 3
 
0.3%
3412 1
 
0.1%
3216 10
 
1.0%
3214 1
 
0.1%
3200 32
3.3%
3168 1
 
0.1%
3120 4
 
0.4%
3088 1
 
0.1%
3080 4
 
0.4%
3040 4
 
0.4%

resolution_width
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1075.852
Minimum480
Maximum2460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-10-22T16:14:59.513486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile720
Q11080
median1080
Q31080
95-th percentile1562
Maximum2460
Range1980
Interquartile range (IQR)0

Descriptive statistics

Standard deviation290.16493
Coefficient of variation (CV)0.2697071
Kurtosis6.4222267
Mean1075.852
Median Absolute Deviation (MAD)0
Skewness1.8183849
Sum1054335
Variance84195.687
MonotonicityNot monotonic
2023-10-22T16:14:59.744892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1080 593
60.5%
720 206
 
21.0%
1440 61
 
6.2%
1170 16
 
1.6%
1600 11
 
1.1%
2400 9
 
0.9%
1284 8
 
0.8%
1290 5
 
0.5%
750 5
 
0.5%
1520 4
 
0.4%
Other values (30) 62
 
6.3%
ValueCountFrequency (%)
480 2
 
0.2%
640 1
 
0.1%
720 206
 
21.0%
750 5
 
0.5%
828 3
 
0.3%
854 3
 
0.3%
1080 593
60.5%
1116 3
 
0.3%
1170 16
 
1.6%
1176 1
 
0.1%
ValueCountFrequency (%)
2460 3
 
0.3%
2408 3
 
0.3%
2400 9
0.9%
2220 1
 
0.1%
2200 2
 
0.2%
2088 1
 
0.1%
1916 4
0.4%
1914 1
 
0.1%
1860 1
 
0.1%
1812 2
 
0.2%

Interactions

2023-10-22T16:14:47.536858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:20.865674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:22.776791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:24.828256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:26.644931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:29.108854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:31.218120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:33.312838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:35.123188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:37.282924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:39.879828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:42.498872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:45.159822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:47.713277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:20.991182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:23.088121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:24.961997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:26.798346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:29.235190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:31.342789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:33.431452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:35.324501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:37.415256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:40.111333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:42.720526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:45.318263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:47.933322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:21.129921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:23.213756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:25.100934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:26.956465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:29.367857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:31.479476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:33.558872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:35.537264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:37.560946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:40.352932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:42.916769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:45.477823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:48.124183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:21.323690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:23.367560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:25.241943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:27.126092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:29.637475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:31.629929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:33.687499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:35.748221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:37.712677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:40.603067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:43.138423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:45.638165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:48.303173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:21.470070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:23.520103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:25.394900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:27.273942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:29.807557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:31.822880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:33.820634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:35.892962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:37.881204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:40.787272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:43.328179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:45.856907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:48.448120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:21.594162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:23.656379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:25.522828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:27.437060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:29.978776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:31.955100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:33.937342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:36.018450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:38.037942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:40.950756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:43.484556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:46.017655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:48.646139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:21.727908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:23.794479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:25.668667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:27.782036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:30.149829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:32.105066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:34.066657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:36.221655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:38.166422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:41.106024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:43.682983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:46.198851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:48.837626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:21.912729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:23.954507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:25.801942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:28.030337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:30.286574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:32.231153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:34.198524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:36.490798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:38.304844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:41.277603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:43.907661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:46.373601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:48.984091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:22.064042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:24.131546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:25.935127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:28.236511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:30.461333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:32.353433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:34.351528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:36.613021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:38.428067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:41.435339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:44.096445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:46.641232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:49.148002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:22.201804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:24.293364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:26.067438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:28.476570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:30.658698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:32.477865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:34.492901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:36.745552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:38.561096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:41.596266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:44.276100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:46.816188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:49.423541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:22.352287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:24.428812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:26.209628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:28.648556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:30.818409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:32.636558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:34.663699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:36.881537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:38.881304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:41.769488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:44.685911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:46.992733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:49.591687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:22.483770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:24.564154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:26.347474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:28.800909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:30.960739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:33.039571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:34.824916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:37.000143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:39.091669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:42.001629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:44.826765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:47.171709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:49.756307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:22.628306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:24.685351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:26.492245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:28.955588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:31.086958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:33.173862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:34.957791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:37.138050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:39.547977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:42.211612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:45.002445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:47.344650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-22T16:14:59.963058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
priceavg_ratingprocessor_speedbattery_capacityfast_chargingram_capacityinternal_memoryscreen_sizerefresh_rateprimary_camera_rearprimary_camera_frontresolution_heightresolution_widthbrand_name5G_or_notprocessor_brandnum_coresfast_charging_availablenum_rear_camerasosextended_memory_available
price1.0000.7720.791-0.3230.6500.7510.7610.3010.5570.3700.5580.6070.6250.5000.2360.2310.2730.1050.0930.3640.341
avg_rating0.7721.0000.662-0.2550.6470.8370.7280.3270.6210.5660.6830.5660.5520.1920.6030.2390.3300.5990.4140.0950.456
processor_speed0.7910.6621.000-0.1890.6440.6150.6070.3520.5470.2970.4710.5020.4950.3930.6760.4390.5560.4650.3260.2780.732
battery_capacity-0.323-0.255-0.1891.000-0.245-0.135-0.1160.3000.0650.159-0.128-0.009-0.1840.6070.1790.2560.3510.2250.2070.3330.150
fast_charging0.6500.6470.644-0.2451.0000.6490.5550.4060.6120.4690.5910.3880.4290.2320.4510.1300.0001.0000.1620.0450.623
ram_capacity0.7510.8370.615-0.1350.6491.0000.7960.4070.5860.5490.6430.5440.5090.3570.5630.2740.3900.5270.3390.1130.499
internal_memory0.7610.7280.607-0.1160.5550.7961.0000.3750.5350.4230.5310.5660.5360.2620.5820.3100.3070.4620.2620.2090.477
screen_size0.3010.3270.3520.3000.4060.4070.3751.0000.4740.3940.2580.3930.3190.5820.2300.2840.4510.3790.3140.3550.360
refresh_rate0.5570.6210.5470.0650.6120.5860.5350.4741.0000.5110.4950.5120.4010.5800.6190.2470.1950.4530.2670.0880.492
primary_camera_rear0.3700.5660.2970.1590.4690.5490.4230.3940.5111.0000.6070.3890.3300.2410.3890.2440.3070.6030.3720.1860.151
primary_camera_front0.5580.6830.471-0.1280.5910.6430.5310.2580.4950.6071.0000.4290.4760.2500.4270.2330.3340.4960.4020.2450.333
resolution_height0.6070.5660.502-0.0090.3880.5440.5660.3930.5120.3890.4291.0000.5960.4600.5410.3680.5610.5920.4090.4920.434
resolution_width0.6250.5520.495-0.1840.4290.5090.5360.3190.4010.3300.4760.5961.0000.3430.5300.2380.2730.5730.3560.1320.420
brand_name0.5000.1920.3930.6070.2320.3570.2620.5820.5800.2410.2500.4600.3431.0000.3530.5100.7990.4190.3570.8250.476
5G_or_not0.2360.6030.6760.1790.4510.5630.5820.2300.6190.3890.4270.5410.5300.3531.0000.7220.2470.3520.3480.1170.505
processor_brand0.2310.2390.4390.2560.1300.2740.3100.2840.2470.2440.2330.3680.2380.5100.7221.0000.7730.4670.3030.7240.494
num_cores0.2730.3300.5560.3510.0000.3900.3070.4510.1950.3070.3340.5610.2730.7990.2470.7731.0000.3640.2760.6890.269
fast_charging_available0.1050.5990.4650.2251.0000.5270.4620.3790.4530.6030.4960.5920.5730.4190.3520.4670.3641.0000.5200.0920.161
num_rear_cameras0.0930.4140.3260.2070.1620.3390.2620.3140.2670.3720.4020.4090.3560.3570.3480.3030.2760.5201.0000.1140.233
os0.3640.0950.2780.3330.0450.1130.2090.3550.0880.1860.2450.4920.1320.8250.1170.7240.6890.0920.1141.0000.295
extended_memory_available0.3410.4560.7320.1500.6230.4990.4770.3600.4920.1510.3330.4340.4200.4760.5050.4940.2690.1610.2330.2951.000

Missing values

2023-10-22T16:14:50.000864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T16:14:50.396695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-22T16:14:50.673836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

brand_namemodelpriceavg_rating5G_or_notprocessor_brandnum_coresprocessor_speedbattery_capacityfast_charging_availablefast_chargingram_capacityinternal_memoryscreen_sizerefresh_ratenum_rear_camerasosprimary_camera_rearprimary_camera_frontextended_memory_availableresolution_heightresolution_width
0appleApple iPhone 11389997.30bionic6.02.653110.00NaN4646.1602ios12.012.001792828
1appleApple iPhone 11 (128GB)469997.50bionic6.02.653110.00NaN41286.1602ios12.012.001792828
2appleApple iPhone 11 Pro Max1099007.70bionic6.02.653500.0118.04646.5603ios12.012.0026881242
3appleApple iPhone 12519997.41bionic6.03.10NaN0NaN4646.1602ios12.012.0025321170
4appleApple iPhone 12 (128GB)559997.51bionic6.03.10NaN0NaN41286.1602ios12.012.0025321170
5appleApple iPhone 12 (256GB)679997.61bionic6.03.10NaN0NaN42566.1602ios12.012.0025321170
6appleApple iPhone 12 Mini409997.41bionic6.03.10NaN0NaN4645.4602ios12.012.0023401080
7appleApple iPhone 12 Mini (128GB)459997.51bionic6.03.10NaN0NaN41285.4602ios12.012.0023401080
8appleApple iPhone 12 Mini (256GB)559997.51bionic6.03.10NaN0NaN42565.4602ios12.012.0023401080
9appleApple iPhone 12 Pro (256GB)1199008.01bionic6.03.10NaN0NaN62566.1603ios12.012.0025321170
brand_namemodelpriceavg_rating5G_or_notprocessor_brandnum_coresprocessor_speedbattery_capacityfast_charging_availablefast_chargingram_capacityinternal_memoryscreen_sizerefresh_ratenum_rear_camerasosprimary_camera_rearprimary_camera_frontextended_memory_availableresolution_heightresolution_width
970xiaomiXiaomi Redmi Note 13 Pro Max 5G204998.31snapdragon8.02.365200.0167.061286.671204android108.032.0124601080
971xiaomiXiaomi Redmi Note 4103006.50snapdragon8.02.004100.00NaN4645.50601android13.05.0119201080
972xiaomiXiaomi Redmi Note 8 202199907.50helio8.02.004000.0118.04646.30604android48.013.0123401080
973xiaomiXiaomi Redmi Note 8 Pro169997.80helio8.02.054500.0118.06646.53604android64.020.0123401080
974xiaomiXiaomi Redmi Note 9119897.50helio8.02.005020.0122.04646.53604android48.013.0123401080
975xiaomiXiaomi Redmi Note 9 Pro139997.50snapdragon8.02.305020.0118.04646.67604android48.016.0124001080
976xiaomiXiaomi Redmi Note 9 Pro (4GB RAM + 128GB)144397.70snapdragon8.02.305020.0118.041286.67604android48.016.0124001080
977xiaomiXiaomi Redmi Note 9 Pro Max164908.00snapdragon8.02.305020.0133.06646.67604android64.032.0124001080
978zteZTE Axon 30S199998.21snapdragon8.03.204200.0155.061286.901204android50.016.0124601080
979zteZTE Axon 40 Ultra 5G619908.91snapdragon8.03.005000.0165.081286.801203android64.016.0024801116